Tagged: deep learning

Google, IBM, Microsoft, Baidu, NEC and others use deep learning and neural networks in development of their most recent speech recognition and image analysis systems. Neural networks have countless other uses, so naturally there are tons of startups trying to use neural networks in new ways. The problem being faced with now, is how exactly to implement neural network models in a way that mimics the circuitry of the brain. Brains are highly parallel and extremely efficient; the ultimate achievement of a neural network model would be able to perform large scale parallel operations while being energy efficient. Current technology is not well suited for large-scale parallel processing, and it is nowhere near as efficient as our brain, which uses only 20 watts of power on average (a typical supercomputer uses somewhere along the order of several megawatts of electricity).

One way in which future computers could mimic the efficiency of the brain is being developed by IBM. IBM believes that energy efficiency is what will guide the next generation of computers, not raw processing power. Current silicon chips have been doubling in power through Moore’s Law for almost half a century, but are now reaching a physical limit. To break through this limit, researchers at IBM’s Zurich lab headed by Dr. Patrick Ruch and Dr. Bruno Michel want to mimic biology’s allometric scaling for new “bionic” computing. Allometric scaling is when an animal’s metabolic power increases with its body size; the approach taken by IBM is to start with 3D computing architecture, with processors stacked and memory in between. In order to keep everything running without overheating, this biologically-inspired computer would be powered, and cooled, by so-called electronic blood. Hopefully this fluid will be able to multi-task, and like blood supplies sugar while taking away heat, accomplish liquid fueling and cooling at the same time.